Modelling landscape‐scale habitat use using GIS and remote sensing: a case study with great bustards

Abstract

Summary 1.Many species are adversely affected by human activities at large spatial scales and their conservation requires detailed information on distributions. Intensive ground surveys cannot keep pace with the rate of land-use change over large areas and new methods are needed for regional-scale mapping. 2. We present predictive models for great bustards in central Spain based on readily available advanced very high resolution radiometer (AVHRR) satellite imagery combined with mapped features in the form of geographic information system (GIS) data layers. As AVHRR imagery is coarse-grained, we used a 12-month time series to improve the definition of habitat types. The GIS data comprised measures of proximity to features likely to cause disturbance and a digital terrain model to allow for preference for certain topographies. 3. We used logistic regression to model the above data, including an autologistic term to account for spatial autocorrelation. The results from models were combined using Bayesian integration, and model performance was assessed using receiver operating characteristics plots. 4. Sites occupied by bustards had significantly lower densities of roads, buildings, railways and rivers than randomly selected survey points. Bustards also occurred within a narrower range of elevations and at locations with significantly less variable terrain. 5. Logistic regression analysis showed that roads, buildings, rivers and terrain all contributed significantly to the difference between occupied and random sites. The Bayesian integrated probability model showed an excellent agreement with the original census data and predicted suitable areas not presently occupied. 6. The great bustard's distribution is highly fragmented and vacant habitat patches may occur for a variety of reasons, including the species' very strong fidelity to traditional sites through conspecific attraction. This may limit recolonization of previously occupied sites. 7. We conclude that AVHRR satellite imagery and GIS data sets have potential to map distributions at large spatial scales and could be applied to other species. While models based on imagery alone can provide accurate predictions of bustard habitats at some spatial scales, terrain and human influence are also significant predictors and are needed for finer scale modelling.

Effect of Climate Change on Lyme Disease Risk in North America

“…As for the national scale model, spatial autocorrelation was accounted for in the model by applying an autologistic approach (Augustin, Mugglestone et al 1996). This methodology incorporates a smoothing filter, called the autologistic term, as an additional covariate in the logistic model (Augustin, Mugglestone et al 1996;Osborne, Alonso et al 2001). Because vector population status in many locations in the United States is unknown, and because we did not include any surveillance data from Canada or Mexico, the model incorporated the modified Gibbs sampler to estimate the distribution in unknown areas (Augustin, Mugglestone et al 1996;Augustin, Mugglestone et al 1998).…”

A climate-based model predicts the spatial distribution of the Lyme disease vector Ixodes scapularis in the United States.

“…A moving window was used to calculate the average probability of occupation among the set of neighbors defined by the limit of correlation, weighted by the inverse of the Euclidean distance. This average probability is called the autologistic term and is added as an additional covariate in the logistic model (Augustin et al 1996;Osborne et al 2001). The autologistic term acts as a smoothing filter, removing isolated pixels and consolidating habitat patches.…”

Rapid evolutionary change in a secondary sexual character linked to climatic change

“…NDVI has been used as an indicator of relative biomass and greenness of the vegetation (Paruelo et al, 1997;Chen & Brutsaert, 1998 6 ;Boone et al, 2000), precipitation (Schmidt & Karnieli, 2000) and quantity and quality of bird habitats (Wallin et al, 1992). Maurer (1994) indicated a correlation between abundance of birds and NDVI, and NDVI has the potential to be used for mapping bird distributions at large spatial scales (Osborne et al, 2001). NDVI data are available at NASA for the entire African continent and for the Mediterranean countries at http://edcintl.…”